Last updated: 2025-11-15
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Knit directory: factor_analysis_new/
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suppressMessages(library(ggplot2))
suppressMessages(library(gridExtra))
suppressMessages(library(tidyr))
suppressMessages(library(forcats))
source("/project/xinhe/xsun/pathway_factor/data_v2/traits_finalselection_SLEremoved.R")
#traits <- c(traits_EUR, traits_EAS)
traits <- c(traits_EUR)
celltypes <- c("B_cell","CD14_positive_monocyte","CD15_positive_leukocyte","platelet","T_cell","thymocyte")
type <- c("B cell (CD19+)","Monocyte (CD14+)","Leukocyte (CD15+)","Platelet","T cell (CD4+)","T cell (CD8+)")
names(type) <- celltypes
names(celltypes) <- type
qqplot <- function(pvalues,title=NULL) {
pval <- pvalues[complete.cases(pvalues)]
title <- title
plotdata <- data.frame(observed = -log10(sort(as.numeric(pval))),
expected = -log10(ppoints(length(pval))))
qq <- ggplot(plotdata) + theme_bw(base_line_size =0.3) +
geom_point(aes(expected, observed), shape = 1, size = 1.5,color = "steelblue") +
geom_abline(intercept = 0, slope = 1, alpha = 0.6, color = "red") +
ggtitle(title) + theme(plot.title = element_text(hjust = 0.5)) +
labs(x = expression(paste("Expected -log"[10],"(p-value)")),
y = expression(paste("Observed -log"[10],"(p-value)"))) +
theme(axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 12, color = "black"),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 12, color = "black") )
return(qq)
}
histogram <- function(pvalues,title=NULL,xlab =NULL) {
p <- as.data.frame(pvalues)
colnames(p) <- "p"
plot <- ggplot(p, aes(x=p)) + geom_histogram(breaks = seq(0, 1, by = 0.05),color="white",fill = "steelblue")+
theme_bw(base_line_size =0.3) +
ggtitle(title) + theme(plot.title = element_text(hjust = 0.5)) +
labs(x = xlab,
y = "Count") +
theme(axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 12, color = "black"),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 12, color = "black"),
text= element_text(family="Times"))
return(plot)
}
qqplot_multi <- function(pvalues_list, legend_names = NULL, colors = NULL, title = NULL) {
# Check if legend names are provided, else use default names
if (is.null(legend_names) || length(legend_names) != length(pvalues_list)) {
legend_names <- paste("Set", seq_along(pvalues_list))
}
# Check if colors are provided, else use rainbow colors
if (is.null(colors) || length(colors) != length(pvalues_list)) {
colors <- rainbow(length(pvalues_list))
}
# Initialize an empty list for storing data frames
plotdata_list <- list()
# Loop through each vector of p-values
for (i in seq_along(pvalues_list)) {
pval <- pvalues_list[[i]][complete.cases(pvalues_list[[i]])]
data <- data.frame(
observed = -log10(sort(as.numeric(pval))),
expected = -log10(ppoints(length(pval))),
set = legend_names[i] # Use provided legend name
)
plotdata_list[[i]] <- data
}
# Combine all data frames into one
plotdata <- do.call(rbind, plotdata_list)
# Plotting
qq <- ggplot(plotdata, aes(x = expected, y = observed, color = set)) +
theme_bw(base_line_size = 0.3) +
geom_point(shape = 1, size = 1.5) +
geom_abline(intercept = 0, slope = 1, alpha = 0.6, color = "black") +
ggtitle(title) +
theme(plot.title = element_text(hjust = 0.5)) +
labs(x = expression(paste("Expected -log"[10], "(p-value)")),
y = expression(paste("Observed -log"[10], "(p-value)")),
color = "Groups") +
scale_color_manual(values = colors) + # Use provided or default colors
theme(axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 12, color = "black"),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 12, color = "black"))
return(qq)
}For all independent SNPs, we generated 100 random samples using vSampler, matching the number of SNPs in LD and MAF. These randomly sampled SNPs were then used to perform factor–SNP association tests, from which we computed ACAT p-values for each factor–SNP pair. The resulting random ACAT p-values served as the null distributions to correct the original ACAT p-values.
We applied this correction at three levels: pair null, trait null, and global null.
When computing ACAT p-values, we used two settings:
folder_acat <- "/project/xinhe/xsun/pathway_factor/analysis/2.ACAT_v2/results/acat_correct_EUR/"
acat_allcelltypes <- list()
for (i in 1:length(celltypes)) {
files_acat <- list.files(path = folder_acat, pattern = celltypes[i], full.names = T)
acat_all <- c()
for(file in files_acat) {
acat <- readRDS(file)
acat$trait <- sub(".*/[^-]+-(.*)_ACAT_corrected\\.RDS$", "\\1", file)
acat$celltype <- celltypes[i]
acat_all <- rbind(acat_all,acat)
}
acat_allcelltypes[[celltypes[i]]] <- acat_all
}celltype <- "B_cell"
acat_celltype <- acat_allcelltypes[[celltype]]
acat_celltype <- acat_celltype[order(acat_celltype$acat_p),]
DT::datatable(acat_celltype,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',paste0('TOP 100 pairs by Original ACAT p-values -- ', type[celltype])),options = list(pageLength = 10) )celltype <- "CD14_positive_monocyte"
acat_celltype <- acat_allcelltypes[[celltype]]
acat_celltype <- acat_celltype[order(acat_celltype$acat_p),]
DT::datatable(acat_celltype,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',paste0('TOP 100 pairs by Original ACAT p-values -- ', type[celltype])),options = list(pageLength = 10) )